{"type":"ScholarlyArticle","title":"Regularisation for Efficient Softmax Parameter Generation in Low-Resource Text Classifiers","authors":[{"name":"Daniel Grießhaber","slug":"daniel-grie-haber","given":"Daniel","family":"Grießhaber","affiliation":"Institute for Applied Artificial Intelligence (IAAI) Stuttgart Media University Nobelstraße 10 , 70569 Stuttgart"},{"name":"Johannes Maucher","slug":"johannes-maucher","given":"Johannes","family":"Maucher","affiliation":"Institute for Applied Artificial Intelligence (IAAI) Stuttgart Media University Nobelstraße 10 , 70569 Stuttgart"},{"name":"Ngoc Thang Vu","slug":"ngoc-thang-vu","given":"Ngoc Thang","family":"Vu","affiliation":"Institute for Natural Language Processing (IMS) University of Stuttgart Pfaffenwaldring 5B , 70569 Stuttgart"}],"abstract":"Meta-learning has made tremendous progress in recent years and was demonstrated to be particularly suitable in low-resource settings where training data is very limited. However, meta-learning models still require large amounts of training tasks to achieve good generalisation. Since labelled training data may be sparse, self-supervision-based approaches are able to further improve performance on downstream tasks. Although no labelled data is necessary for this training, a large corpus of unlabelled text needs to be available.  \r\nIn this paper, we improve on recent advances in meta-learning for natural language models that allow training on a diverse set of training tasks for few-shot, low-resource target tasks. We introduce a way to generate new training data with the need for neither more supervised nor unsupervised datasets. We evaluate the method on a diverse set of NLP tasks and show that the model decreases in performance when trained on this data without further adjustments. Therefore, we introduce and evaluate two methods for regularising the training process and show that they not only improve performance when used in conjunction with the new training data but also improve average performance when training only on the original data, compared to the baseline.","doi":"10.24963/ijcai.2023/562","url":"https://ijcai.org/proceedings/2023/562","pdf":"https://ijcai.org/proceedings/2023/0562.pdf","markdown":"https://ijcai.org/proceedings/2023/562.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/562","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Natural Language Processing","track":"Main Track","number":562,"pages":{"first":5058,"last":5066},"keywords":["Machine Learning: ML: Meta-learning","Natural Language Processing: NLP: Text classification","Machine Learning: ML: Few-shot learning"],"videos":[],"references":[{"position":1,"raw":"[Andrychowicz et al., 2016] Marcin Andrychowicz, Misha Denil, Sergio Gómez Colmenarejo, Matthew W. Hoff- man, David Pfau, Tom Schaul, Brendan Shillingford, and Nando de Freitas. Learning to learn by gradient descent by gradient descent. In NIPS, 2016.","title":"Learning to learn by gradient descent by gradient descent","authors":"Marcin Andrychowicz, Misha Denil, Sergio Gómez Colmenarejo, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando De Freitas","year":"2016","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Antoniou et al., 2019] Antreas Antoniou, Harrison Ed- wards, and Amos J. Storkey. How to train your MAML. In ICLR, 2019.","title":"How to train your MAML","authors":"Antreas Antoniou, Harrison Edwards, Amos J Storkey","year":"2019","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bahdanau et al., 2015] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. In ICLR, 2015.","title":"Neural machine translation by jointly learning to align and translate","authors":"Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio","year":"2015","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bansal et al., 2020a] Trapit Bansal, Rishikesh Jha, and An- drew McCallum. Learning to few-shot learn across diverse natural language classification tasks. In ICCL, 2020.","title":"Trapit Bansal, Rishikesh Jha, and Andrew McCallum. Learning to few-shot learn across diverse natural language classification tasks","authors":"Bansal","year":"2020","venue":"ICCL","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bansal et al., 2020b] Trapit Bansal, Rishikesh Jha, Tsend- suren Munkhdalai, and Andrew McCallum. Self- supervised meta-learning for few-shot natural language classification tasks. In EMNLP, 2020.","title":"Selfsupervised meta-learning for few-shot natural language classification tasks","authors":"Bansal","year":"2020","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Bansal et al., 2021] Trapit Bansal, Karthick Prasad Gu- nasekaran, Tong Wang, Tsendsuren Munkhdalai, and An- drew McCallum. Diverse distributions of self-supervised tasks for meta-learning in NLP. In EMNLP, 2021.","title":"Diverse distributions of self-supervised tasks for meta-learning in NLP","authors":"Trapit Bansal, Karthick Prasad Gunasekaran, Tong Wang, Tsendsuren Munkhdalai, Andrew Mccallum","year":"2021","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Bao et al., 2020] Yujia Bao, Menghua Wu, Shiyu Chang, and Regina Barzilay. Few-shot text classification with distributional signatures. In International Conference on Learning Representations, 2020.","title":"Few-shot text classification with distributional signatures","authors":"Yujia Bao, Menghua Wu, Shiyu Chang, Regina Barzilay","year":"2020","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Bechtle et al., 2021] Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav Sukhatme, and Franziska Meier. Meta-learning via learned loss. In ICPR, 2021.","title":"Meta-learning via learned loss","authors":"Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav Sukhatme, Franziska Meier","year":"2021","venue":"ICPR","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Bengio et al., 1991] Y. Bengio, S. Bengio, and J. Cloutier. Learning a synaptic learning rule. In IJCNN, 1991.","title":"Learning a synaptic learning rule","authors":"Y Bengio, S Bengio, J Cloutier","year":"1991","venue":"IJCNN","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Bertinetto et al., 2019] Luca Bertinetto, Joao F. Henriques, Philip Torr, and Andrea Vedaldi. Meta-learning with dif- ferentiable closed-form solvers. In ICLR, 2019.","title":"Meta-learning with differentiable closed-form solvers","authors":"Luca Bertinetto, Joao F Henriques, Philip Torr, Andrea Vedaldi","year":"2019","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Blitzer et al., 2007] John Blitzer, Mark Dredze, and Fer- nando Pereira. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In ACL, 2007.","title":"Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification","authors":"John Blitzer, Mark Dredze, Fernando Pereira","year":"2007","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Bouazizi and Ohtsuki, 2016] Mondher Bouazizi and To- moaki Ohtsuki. Sentiment analysis: From binary to multi- class classification: A pattern-based approach for multi- class sentiment analysis in twitter. In IEEE ICC, 2016.","title":"Sentiment analysis: From binary to multiclass classification: A pattern-based approach for multiclass sentiment analysis in twitter","authors":"Ohtsuki Bouazizi, Mondher Bouazizi, Tomoaki Ohtsuki","year":"2016","venue":"IEEE ICC","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Bowman et al., 2015] Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. A large annotated corpus for learning natural language inference. In ACL, 2015.","title":"A large annotated corpus for learning natural language inference","authors":"R Samuel, Gabor Bowman, Christopher Angeli, Christopher D Potts, Manning","year":"2015","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Brown et al., 2020] Tom Brown, Benjamin Mann, Nick Ry- der, Melanie Subbiah, Jared D Kaplan, Prafulla Dhari- wal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learn- ers. In NIPS, 2020.","title":"Language models are few-shot learners","authors":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam Mccandlish, Alec Radford, Ilya Sutskever, Dario Amodei","year":"2020","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Chechik et al., 2010] Gal Chechik, Varun Sharma, Uri Shalit, and Samy Bengio. Large scale online learning of image similarity through ranking. JMLR, 2010.","title":"Large scale online learning of image similarity through ranking","authors":"Chechik","year":"2010","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Cheng et al., 2016] Jianpeng Cheng, Li Dong, and Mirella Lapata. Long short-term memory-networks for machine reading. In EMNLP, 2016.","title":"Long short-term memory-networks for machine reading","authors":"Cheng","year":"2016","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Cui et al., 2021] Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, and Ziqing Yang. Pre-training with whole word masking for chinese BERT. IEEE/ACM Trans. Audio, Speech and Lang. Proc., 2021.","title":"Pre-training with whole word masking for chinese BERT","authors":"Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang","year":"2021","venue":"IEEE/ACM Trans. Audio, Speech and Lang. Proc","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Cutkosky and Orabona, 2019] Ashok Cutkosky and Francesco Orabona. Momentum-based variance reduction in non-convex SGD. In NIPS, 2019.","title":"Momentum-based variance reduction in non-convex SGD","authors":"Ashok Cutkosky, Francesco Orabona","year":"2019","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Deng et al., 2009] Jia Deng, Wei Dong, Richard Socher, Li- Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In IEEE CVPR, 2009.","title":"Imagenet: A large-scale hierarchical image database","authors":"Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei","year":"2009","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Devlin et al., 2019] Jacob Devlin, Ming-Wei Chang, Ken- ton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understand- ing. In NAACL, 2019.","title":"BERT: Pre-training of deep bidirectional transformers for language understanding","authors":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova","year":"2019","venue":"NAACL","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Fang and Cohn, 2017] Meng Fang and Trevor Cohn. Model transfer for tagging low-resource languages using a bilin- gual dictionary. In ACL, 2017.","title":"Model transfer for tagging low-resource languages using a bilingual dictionary","authors":"Meng Fang, Trevor Cohn","year":"2017","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Finn et al., 2017] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In PMLR, 2017.","title":"Model-agnostic meta-learning for fast adaptation of deep networks","authors":"Finn","year":"2017","venue":"PMLR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Ganin et al., 2016] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Franc ¸ois Laviolette, Mario March, and Victor Lempitsky. Domain- adversarial training of neural networks. JMLR, 2016.","title":"Domainadversarial training of neural networks","authors":"Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Mario Franc ¸ois Laviolette, Victor March, Lempitsky","year":"2016","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Han et al., 2021] Chengcheng Han, Zeqiu Fan, Dongxiang Zhang, Minghui Qiu, Ming Gao, and Aoying Zhou. Meta- learning adversarial domain adaptation network for few- shot text classification. In IJCNLP, 2021.","title":"Metalearning adversarial domain adaptation network for fewshot text classification","authors":"Han","year":"2021","venue":"IJCNLP","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Hochreiter et al., 2001] Sepp Hochreiter, A. Steven Younger, and Peter R. Conwell. Learning to learn using gradient descent. In ICANN, 2001.","title":"Learning to learn using gradient descent","authors":"A Steven Sepp Hochreiter, Peter R Younger, Conwell","year":"2001","venue":"ICANN","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Krause et al., 2013] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine- grained categorization. In IEEE Workshop on 3D Repre- sentation and Recognition, 3dRR-13 2013, 2013.","title":"3d object representations for finegrained categorization","authors":"Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei","year":"2013","venue":"IEEE Workshop on 3D Representation and Recognition","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Li and Malik, 2017] Ke Li and Jitendra Malik. Learning to optimize. In ICLR, 2017.","title":"Learning to optimize","authors":"Ke Li, Jitendra Malik","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Li and Zhang, 2021] Yue Li and Jiong Zhang. Semi- supervised meta-learning for cross-domain few-shot intent classification. In 1st Workshop on Meta Learning and Its Applications to Natural Language Processing, 2021.","title":"Semisupervised meta-learning for cross-domain few-shot intent classification","authors":"Yue Li, Jiong Zhang","year":"2021","venue":"1st Workshop on Meta Learning and Its Applications to Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Liu et al., 2020] Zequn Liu, Ruiyi Zhang, Yiping Song, and Ming Zhang. When does MAML work the best? an em- pirical study on model-agnostic meta-learning in NLP ap- plications. CoRR, abs/2005.11700, 2020.","title":"When does MAML work the best? an empirical study on model-agnostic meta-learning in NLP applications","authors":"Liu","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Luong et al., 2015] Thang Luong, Hieu Pham, and Christo- pher D. Manning. Effective approaches to attention-based neural machine translation. In EMNLP, 2015.","title":"Effective approaches to attention-based neural machine translation","authors":"Thang Luong, Hieu Pham, Christopher D Manning","year":"2015","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Munkhdalai and Yu, 2017] Tsendsuren Munkhdalai and Hong Yu. Meta networks. In PMLR, 2017.","title":"Tsendsuren Munkhdalai and Hong Yu. Meta networks","authors":"Yu Munkhdalai","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Nichol et al., 2018] Alex Nichol, Joshua Achiam, and John Schulman. On first-order meta-learning algorithms. CoRR, abs/1803.02999, 2018.","title":"On first-order meta-learning algorithms","authors":"Alex Nichol, Joshua Achiam, John Schulman","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Pires et al., 2019] Telmo Pires, Eva Schlinger, and Dan Gar- rette. How multilingual is multilingual BERT? In ACL, 2019.","title":"How multilingual is multilingual BERT?","authors":"Pires","year":"2019","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Radford et al., 2019] Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. OpenAI Blog, 2019.","title":"Language models are unsupervised multitask learners","authors":"Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever","year":"2019","venue":"OpenAI Blog","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Ravi and Larochelle, 2017] Sachin Ravi and Hugo Larochelle. Optimization as a model for few-shot learning. In ICLR, 2017.","title":"Optimization as a model for few-shot learning","authors":"Larochelle ; Ravi, Sachin Ravi, Hugo Larochelle","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Salameh et al., 2015] Mohammad Salameh, Saif Moham- mad, and Svetlana Kiritchenko. Sentiment after trans- lation: A case-study on arabic social media posts. In NAACL, 2015.","title":"Sentiment after translation: A case-study on arabic social media posts","authors":"Mohammad Salameh, Saif Mohammad, Svetlana Kiritchenko","year":"2015","venue":"NAACL","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Santoro et al., 2016] Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lill- icrap. Meta-learning with memory-augmented neural networks. In ICML, 2016.","title":"Meta-learning with memory-augmented neural networks","authors":"Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap","year":"2016","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Schmidhuber, 1987] Jurgen Schmidhuber. Evolutionary principles in self-referential learning. on learning now to learn: The meta-meta-meta...-hook. Diploma thesis, Tech- nische Universitat Munchen, Germany, 1987.","title":"Jurgen Schmidhuber. Evolutionary principles in self-referential learning. on learning now to learn: The meta-meta-meta","authors":"Schmidhuber","year":"1987","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Schroff et al., 2015] Florian Schroff, Dmitry Kalenichenko, and James Philbin. FaceNet: A unified embedding for face recognition and clustering. IEEE CVPR, 2015.","title":"FaceNet: A unified embedding for face recognition and clustering","authors":"Schroff","year":"2015","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Schultz and Joachims, 2003] Matthew Schultz and Thorsten Joachims. Learning a distance metric from relative comparisons. In NIPS, 2003.","title":"Learning a distance metric from relative comparisons","authors":"Matthew Schultz, Thorsten Joachims","year":"2003","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Snell et al., 2017] Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. In NIPS, 2017.","title":"Prototypical networks for few-shot learning","authors":"Jake Snell, Kevin Swersky, Richard Zemel","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Sukhbaatar et al., 2015] Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus. End-to-end memory networks. In NIPS, 2015.","title":"End-to-end memory networks","authors":"Sukhbaatar","year":"2015","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Sung et al., 2018] Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip Torr, and Timothy Hospedales. Learn- ing to compare: Relation network for few-shot learning. In IEEE CVPR, 2018.","title":"Learning to compare: Relation network for few-shot learning","authors":"Sung","year":"2018","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Tan and Zhang, 2008] Songbo Tan and Jin Zhang. An em- pirical study of sentiment analysis for chinese documents. Expert Systems with Applications, 2008.","title":"An empirical study of sentiment analysis for chinese documents","authors":"Zhang Tan, Songbo Tan, Jin Zhang","year":"2008","venue":"Expert Systems with Applications","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Tjong Kim Sang and De Meulder, 2003] Erik F. Tjong Kim Sang and Fien De Meulder. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In HLT-NAACL, 2003.","title":"Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition","authors":"Tjong Kim, Sang, De Meulder, ; ] Erik, F Tjong, Kim Sang, Fien De, Meulder","year":"2003","venue":"HLT-NAACL","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[van der Maaten and Hinton, 2008] Laurens van der Maaten and Geoffrey Hinton. Viualizing data using t-SNE. JMLR, 2008.","title":"Viualizing data using t-SNE","authors":"Van Der Maaten, Laurens Hinton, Geoffrey Van Der Maaten, Hinton","year":"2008","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NIPS, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Vinyals et al., 2016] Oriol Vinyals, Charles Blundell, Tim- othy Lillicrap, koray kavukcuoglu, and Daan Wierstra. Matching networks for one shot learning. In NIPS, 2016.","title":"Matching networks for one shot learning","authors":"Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra","year":"2016","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Wan, 2008] Xiaojun Wan. Using bilingual knowledge and ensemble techniques for unsupervised chinese sentiment analysis. In EMNLP, 2008.","title":"Using bilingual knowledge and ensemble techniques for unsupervised chinese sentiment analysis","authors":"Xiaojun Wan","year":"2008","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Wang et al., 2018] Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. GLUE: A multi-task benchmark and analysis platform for natural language understanding. In EMNLP Workshop BlackboxNLP, 2018.","title":"GLUE: A multi-task benchmark and analysis platform for natural language understanding","authors":"Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel Bowman","year":"2018","venue":"EMNLP Workshop BlackboxNLP","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Wang et al., 2021a] Haoxiang Wang, Han Zhao, and Bo Li. Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation. In ICML, 2021.","title":"Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation","authors":"Wang","year":"2021","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Wang et al., 2021b] Lingxiao Wang, Kevin Huang, Tengyu Ma, Quanquan Gu, and Jing Huang. Variance-reduced first-order meta-learning for natural language processing tasks. In NAACL, 2021.","title":"Variance-reduced first-order meta-learning for natural language processing tasks","authors":"Wang","year":"2021","venue":"NAACL","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Xiao et al., 2021] Yubei Xiao, Ke Gong, Pan Zhou, Guolin Zheng, Xiaodan Liang, and Liang Lin. Adversarial meta sampling for multilingual low-resource speech recogni- tion. AAAI, 2021.","title":"Adversarial meta sampling for multilingual low-resource speech recognition","authors":"Yubei Xiao, Ke Gong, Pan Zhou, Guolin Zheng, Xiaodan Liang, Liang Lin","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Yang et al., 2016] Zichao Yang, Diyi Yang, Chris Dyer, Xi- aodong He, Alex Smola, and Eduard Hovy. Hierarchical attention networks for document classification. In NAACL, 2016.","title":"Hierarchical attention networks for document classification","authors":"Yang","year":"2016","venue":"NAACL","doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Yogatama et al., 2019] Dani Yogatama, Cyprien de Mas- son d'Autume, Jerome T. Connor, Tomás Kociský, Mike Chrzanowski, Lingpeng Kong, Angeliki Lazaridou, Wang Ling, Lei Yu, Chris Dyer, and Phil Blunsom. Learning and evaluating general linguistic intelligence. CoRR, 2019.","title":"Learning and evaluating general linguistic intelligence","authors":"Dani Yogatama, Cyprien De Masson D'autume, Jerome T Connor, Tomás Kociský, Mike Chrzanowski, Lingpeng Kong, Angeliki Lazaridou, Wang Ling, Lei Yu, Chris Dyer, Phil Blunsom","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Zhang et al., 2020] Xuejun Zhang, Yujiang Li, Pengyuan Zhang, and Yonghong Yan. Lingual-agnostic meta- learning for low-resource part-of-speech tagging. In ACL ICITS: IoT and Smart City, 2020.","title":"Lingual-agnostic metalearning for low-resource part-of-speech tagging","authors":"Xuejun Zhang, Yujiang Li, Pengyuan Zhang, Yonghong Yan","year":"2020","venue":"ACL ICITS: IoT and Smart City","doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Zhou et al., 2017] Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba. Places: A 10 million image database for scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 2017.","title":"Places: A 10 million image database for scene recognition","authors":"Zhou","year":"2017","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}